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Efficient Bayesian inference for stochastic time-varying copula models
Carlos Almeida,
Claudia Czado
Associate Professorship of Applied Mathematical Statistics
Technical University of Munich
Research output
:
Contribution to journal
›
Article
›
peer-review
39
Scopus citations
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Dive into the research topics of 'Efficient Bayesian inference for stochastic time-varying copula models'. Together they form a unique fingerprint.
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Keyphrases
Bayesian Inference
100%
Copula
100%
Stochastic Time
100%
Time-varying Copula Model
100%
Latent Variables
66%
Bayesian Approach
33%
Maximum Likelihood Estimation
33%
Simulation Study
33%
Nave
33%
Autoregressive Model
33%
Modeling Framework
33%
Sampling Algorithm
33%
Point Estimate
33%
Credible Interval
33%
Coarse Grid
33%
Monte Carlo (MC) Simulation
33%
Metropolis-Hastings
33%
Gaussian Copula
33%
Time-varying Dependence
33%
Multivariate Financial Time Series
33%
Clayton Copula
33%
Financial Stock Index
33%
Credibility Intervals
33%
Time-varying Copula
33%
Fisher's Z-transformation
33%
Gumbel Copula
33%
Mathematics
Bayesian Inference
100%
Copula
100%
Stochastics
100%
Gaussian Distribution
16%
Markov Chain Monte Carlo
16%
Pointwise
16%
Credible Interval
16%
Simulation Study
16%
Autoregressive Model
16%
Point Estimate
16%
Bayesian Approach
16%
Gibbs Free Energy
16%
Maximum Likelihood
16%